Third, we made choices about which cell types to base measurements on throughout our quantification process. For example, when characterizing the curviness of stripe–interstripe boundaries, we could have based our measurements on \(M\)  or \(X^\ell \)  cells (i.e., curviness of black stripe boundaries), \(X^\text{d}\)  cells (i.e., curviness of gold interstripe boundaries), or a combination of cells of several types. Like McGuirl et al. [1]}, we chose to focus on \(X^\text{d}\)  cells. The overlap of \(M\)  and \(X^\text{d}\)  cells at stripe–interstripe boundaries in CA [2]} patterns makes it challenging not only to visually trace out boundaries in CA patterns, but also to define (inter)stripe width. When looking at a pattern like the one in Figure REF (a), where does the top interstripe end and the stripe begin? What cells are part of interstripes, and what cells—if any—are present in spots? Indeed, when observing large sets of simulated patterns from the ABM [3]} and particularly the CA [2]}, we found it very difficult to consistently judge model output visually. This further highlights the value of our automated approach and careful consideration of hyper-parameters when quantifying messy, cell-based patterns at large scale.
